Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.
The detection of mental fatigue is an important issue in the nascent field of neuroergonomics. Although machine learning approaches and especially deep learning designs have constantly demonstrated their efficiency to automatically detect critical features from raw data, the computational resources for training and predictions are usually very demanding. In this work, we propose a shallow convolutional neural network, with three convolutional layers, for fatigue detection using electroencephalogram (EEG) data that can alleviate the computational burden and provide fast mental fatigue detection. As such, a deep learning model was created utilizing time-frequency domain features, extracted with Morlet wavelet analysis. These features, combined with the higher-level characteristics learnt by the model, resulted in a resilient solution, able to attain very high prediction accuracy (97%), while reducing training time and computing costs. Moreover, by incorporating a subsequent SHAP values analysis on the characteristics that contributed in the model creation, indications of low frequency (theta and alpha band) brain wave characteristics were indicated as prominent mental fatigue detectors.
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